A Trend Detection-Based Auto-Scaling Method for Containers in High-Concurrency Scenarios - Haipeng Liu; Wenhao Zhu; Siyi Fu; Yongjun Lu (IEEE Access)
Cloud computing technology is widely embraced due to its ability to meet diverse computing resource demands from users. As the user base grows, the challenge for cloud service providers to dynamically allocate resources to applications according to real-time customer needs becomes more daunting. Existing auto-scaling mechanisms, such as Horizontal Pod Autoscaler (HPA), lack flexibility in handling transient and irregular fluctuations in resource demands, making it difficult to respond quickly. This research focuses on auto-scaling solutions for applications in high-concurrency scenarios. The study enhances the MAPE structure of traditional auto-scaling models and introduces a trend detection module in a proactive auto-scaling engine based on predictive algorithms. This module detects trends during transient irregular request volume fluctuations, corrects prediction algorithm results inconsistent with the current trend, and provides more reasonable resource allocation for programs. Experimental results, using both real access peak data and simulated data in a Kubernetes environment, indicate that the proposed auto-scaler effectively prevents resource shortages in high-concurrency network environments compared to other auto-scaling mechanisms. It ensures the performance and availability of applications while reducing resource wastage.